Choosing a cloud instance type for a task is still largely a heuristic exercise. While public pricing and hardware specifications are available, they are fragmented, inconsistently structured, and challenging to compare across (or even within) cloud providers -- especially once real workload performance is taken into account. In this talk, we present Spare Cores Navigator, a Python-queryable and open-source benchmark dataset that covers thousands of cloud server types from multiple vendors, with standardized performance and cost-efficiency metrics. We demonstrate how instance selection can be expressed as a simple data query, e.g. filtering by workload characteristics, hardware or compliance constraints, and budget, then ranking candidates by price-performance.
Gergely Daroczi, PhD, has been a passionate open-source package developer for two decades. With over 15 years in the fintech, adtech, healthtech, and other SaaS industries, he has expertise in data science and engineering, as well as cloud infrastructure, with a focus on building scalable data platforms. Gergely maintains a dozen open-source R and Python projects and organizes a tech meetup with 1,800 members in Hungary -- along with other open-source and data conferences.
